# Class 05: Data Visualization
# install.packages("ggplot2")
library(ggplot2)
head(cars)
## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10
# All ggplot have at lieast 3 layers,
# data + aes + geoms
p <- ggplot(data = cars) +
aes(x = speed, y = dist) +
geom_point() +
labs(title = "Stopping Distance of Old Cars",
x = "Speed (MPH)",
y = "Stopping Distance (ft)") +
geom_smooth(method = "lm", formula = 'y ~ x', aes(x = speed, y = dist))
show(p)

# Side-note: ggplot is not the only graphics systems
# a very popular one is good old "base" R graphics
plot(cars)

# Plot some gene expression results.
# Dataset is online in tab separated format so we
# use the read.delim() function to import into R
url <- "https://bioboot.github.io/bimm143_S20/class-material/up_down_expression.txt"
genes <- read.delim(url)
head(genes)
## Gene Condition1 Condition2 State
## 1 A4GNT -3.6808610 -3.4401355 unchanging
## 2 AAAS 4.5479580 4.3864126 unchanging
## 3 AASDH 3.7190695 3.4787276 unchanging
## 4 AATF 5.0784720 5.0151916 unchanging
## 5 AATK 0.4711421 0.5598642 unchanging
## 6 AB015752.4 -3.6808610 -3.5921390 unchanging
# Q. How many genes in this dataset
nrow(genes)
## [1] 5196
# Q. How many genes are "up"?
table(genes$State)
##
## down unchanging up
## 72 4997 127
# Q. What % are up?
round(table(genes$State)/nrow(genes) * 100, 2)
##
## down unchanging up
## 1.39 96.17 2.44
p <- ggplot(genes, aes(x = Condition1, y = Condition2, col = State)) +
geom_point()
show(p)

p <- p + scale_color_manual(values = c("blue", "grey", "red")) +
labs(title = "Gene Expression Changes Upon Drug Treatment",
x = "Control (no drug",
y = "Drug Treatment")
show(p)

# Let's explore the gapminder dataset
# install.packages("gapminder")
library(gapminder)
head(gapminder)
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
# Let's make a new plot of year vs lifeExp
p <- ggplot(gapminder, aes(x = year, y = lifeExp, col = continent)) +
geom_jitter(width = 0.3, alpha = 0.4) +
geom_violin(aes(group = year), alpha = 0.2,
draw_quantiles = 0.5)
show(p)

# Install the plotly
# install.packages("plotly")
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
ggplotly()